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null (Ed.)Given a spatial graph, an origin and a destination, and onboard diagnostics (OBD) data, the energyefficient path selection problem aims to find the path with the least expected energy consumption (EEC). Two main objectives of smart cities are sustainability and prosperity, both of which benefit from reducing the energy consumption of transportation. The challenges of the problem include the dependence of EEC on the physical parameters of vehicles, the autocorrelation of the EEC on segments of paths, the high computational cost of EEC estimation, and potential negative EEC. However, the current cost estimation models for the path selection problem do not consider vehicles’ physical parameters. Moreover, the current path selection algorithms follow the “path + edge” pattern when exploring candidate paths, resulting in redundant computation. Our preliminary work introduced a physicsguided energy consumption model and proposed a maximalfrequentedpathgraph shortestpath algorithm using the model. In this work, we propose an informed algorithm using an admissible heuristic and propose an algorithm to handle negative EEC. We analyze the proposed algorithms theoretically and evaluate the proposed algorithms via experiments with realworld and synthetic data. We also conduct two case studies using realworld data and a road test to validate the proposed method.more » « less

Li, Yan ; Kotwal, Pratik ; Wang, Pengyue ; Shekhar, Shashi ; Northrop, William ( , SSTD '19: Proceedings of the 16th International Symposium on Spatial and Temporal Databases)The trajectoryaware lowestcost path selection problem aims to find the lowestcost path using trajectory data. Trajectory data is valuable since it carries information about travel cost along paths, and also reflects travelers' routing preference. Pathcentric travel cost estimation models using trajectory data grows popular recently, which considers the autocorrelation of the energy consumption on different segments of a path. However, pathcentric models are more computationally expensive than edgecentric models. The main challenge of this problem is that the travel cost of every candidate path explored during the process of searching for the lowestcost path need to be estimated, resulting in high computational cost. The current path selection algorithms that use pathcentric cost estimation models still follow the pattern of "path + edge" when exploring candidate paths, which may result in redundant computation. We introduce a trajectoryaware graph model in which each node is a maximal trajectoryaware path. Two nodes in the trajectoryaware graph are linked by an edge if their union forms a trajectoryunion path. We then propose a path selection algorithm to find a path in the proposed trajectoryaware graph which corresponds to the lowestcost path in the input spatial network. We prove theoretically the proposed algorithm is correct and complete. Moreover, we prove theoretically that the proposed path selection algorithm cost much less computational time than the algorithm used in the related work, and validate it through experiments using realworld trajectory data.more » « less